AWS Certified AI Practitioner Practice Exam

In the context of AI, what does the term "inference" refer to?

1. A. Training the model on historical data

2. B. The deployment of the model into a production environment

3. C. Making predictions based on new input data

4. D. Adjusting the model to eliminate bias

The correct answer is: C. Making predictions based on new input data

The term "inference" in the context of AI specifically refers to the process of making predictions based on new input data using a trained model. Once a model has been adequately trained on historical data, it can be deployed to make predictions or decisions when presented with new data. This stage is crucial in the application of AI because it is where the model's learned patterns are applied to real-world scenarios. For example, in a machine learning model that predicts customer churn, inference would involve taking the current data of a customer and determining the likelihood that they will leave the service based on patterns observed during training. The focus here is on utilizing the model’s capabilities to derive insights or outputs from fresh inputs. In contrast, other choices focus on different aspects of the AI lifecycle. Training a model on historical data is the initial phase that involves feeding it data to learn from it. Deploying a model into production signifies that the model has been fully validated and is now operational. Adjusting the model to eliminate bias involves refining the model to ensure fair predictions, which is a distinct process from inference itself. Each of these processes is integral to the AI implementation pipeline, but inference specifically pertains to the application of a trained model on new data.